class_docstr: Principal Component Analysis (PCA).
  
  Parameters
  ----------
  rescale_with_mean
      Whether or not to subtract each column's mean before performing SVD.
  rescale_with_std
      Whether or not to standardize each column before performing SVD.
  n_components
      The number of principal components to compute.
  n_iter
      The number of iterations used for computing the SVD.
  copy
      Whether nor to perform the computations inplace.
  check_input
      Whether to check the coherence of the inputs or not.
docstr: Returns the cosine similarities between the rows and their principal components.
  
  The row cosine similarities are obtained by calculating the cosine of the angle shaped by
  the row principal coordinates and the row principal components. This is calculated by
  squaring each row projection coordinate and dividing each squared coordinate by the sum of
  the squared coordinates, which results in a ratio comprised between 0 and 1 representing
  the squared cosine.
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code: def row_cosine_similarities(self, X):
      """Returns the cosine similarities between the rows and their principal components.
  
      The row cosine similarities are obtained by calculating the cosine of the angle shaped by
      the row principal coordinates and the row principal components. This is calculated by
      squaring each row projection coordinate and dividing each squared coordinate by the sum of
      the squared coordinates, which results in a ratio comprised between 0 and 1 representing
      the squared cosine.
  
      """
      squared_coordinates = np.square(self._scale(X)).sum(axis=1)
      return (self.row_coordinates(X) ** 2).div(
          squared_coordinates, axis=0
      )
  
imports: from __future__ import annotations
  import functools
  import altair
  import numpy
  import pandas
  import sklearn.base
  import sklearn.utils
  from sklearn import preprocessing
  from prince import svd, utils
label_desc: Use image node_id 11 for calling the PCA obj's underlying member method code with example usage: obj.row_cosine_similarities(X) and returns: unknown